A method for reducing data rate for low-speed target detection under high repetition frequency

Abstract

The application belongs to the technical field of low-speed target detection. The application provides a method for reducing data rate for low-speed target detection under high pulse repetition frequency. In the method, N PRI pulse accumulation points in one CPI phase accumulation time are added and combined into one equivalent PRI data, the original N PRI pulse accumulation points are reduced to one pulse for phase accumulation, and the data rate is reduced to the original 1 / N. The method reduces data transmission bandwidth and calculation amount while causing less loss of signal-to-noise ratio for low-speed target detection. Under the condition of less loss of signal-to-noise ratio for low-speed target detection, the method reduces data rate for low-speed target detection such as unmanned aerial vehicle under high pulse repetition frequency, reduces data transmission bandwidth and calculation amount, reduces operation time, and saves hardware cost.

Description

Technical Field

[0001] This disclosure relates to the field of low-speed target detection technology, and more particularly to a method for reducing the data rate for low-speed target detection under high repetition rate. Background Technology

[0002] In some radar detection scenarios, it is necessary to detect both high-speed and low-speed targets. Therefore, the system is designed with a high radar pulse repetition frequency to reduce velocity ambiguity in high-speed target detection. Low-speed target detection requires a longer accumulation time to improve velocity resolution. The high radar pulse repetition frequency brings a large number of coherent accumulations to low-speed target detection, which greatly increases the amount of computation and leads to computational redundancy.

[0003] Therefore, it is necessary to improve one or more of the problems existing in the above-mentioned related technical solutions.

[0004] It should be noted that this section is intended to provide background or context for the technical solutions of this disclosure as set forth in the claims. The description herein does not constitute an admission that it is prior art simply because it is included in this section. Summary of the Invention

[0005] The purpose of this disclosure is to provide a method for reducing the data rate for low-speed target detection at high repetition rates, thereby overcoming at least to some extent one or more problems caused by the limitations and defects of related technologies.

[0006] According to a first aspect of the present disclosure, a method for reducing the data rate for low-speed target detection at high repetition rates is provided, comprising:

[0007] Obtain the pulse accumulation data of N pulse repetition intervals PRI within a coherent processing interval CPI; where N is a positive integer;

[0008] The pulse accumulation data of N PRIs are added together by combining the data of the adjacent M PRIs to form an equivalent PRI dataset, thereby reducing the number of pulse accumulation points of the original N PRIs to [number missing]. Each pulse is used for coherent accumulation, reducing the data rate to 1 / M of the original value; where M is a positive integer.

[0009] Furthermore, the conditions for determining M are:

[0010] Evaluate whether the data transfer time and computation time when using the original N PRI data meet the system requirements. If not, determine the value of M based on the system's data transfer bandwidth and processing capacity to reduce the data rate.

[0011] The evaluation assesses whether the signal-to-noise ratio loss for targets at different speeds after adding and merging adjacent M PRI data meets the radar detection performance requirements.

[0012] Choose the smallest M value that satisfies both of the above conditions.

[0013] Furthermore, assessing whether the data transmission time meets the requirements includes:

[0014] Based on the radar system's channel sampling rate, number of channels, single-channel data bit width, and effective data transmission rate of the system bus, calculate the data rate of the original N PRI data.

[0015] The data rate of the original N PRI data is compared with the system bus bandwidth to determine whether the data rate needs to be reduced.

[0016] Furthermore, assessing whether the computation time meets the requirements includes:

[0017] The processing time is halved for every reduction of the number of pulses.

[0018] By combining the processing unit's calculation time for N pulses, the number of pulses that need to be reduced is determined, and thus the value of M is calculated.

[0019] Furthermore, assessing whether the signal-to-noise ratio loss meets the radar detection performance requirements includes:

[0020] Calculate the signal-to-noise ratio loss based on the target velocity and the unambiguous velocity of the system;

[0021] Ensure that the signal-to-noise ratio after loss is greater than the minimum detectable signal-to-noise ratio of the system.

[0022] Furthermore, the expression for the signal-to-noise ratio loss is:

[0023]

[0024] in, For the target speed, For the system to be unambiguous speed.

[0025] Furthermore, the choice of M must satisfy:

[0026]

[0027] in, For radar performance signal-to-noise ratio, The target echo signal-to-noise ratio is the result of processing the original N pulses.

[0028] Furthermore, radar systems are used for detecting low-speed targets by unmanned aerial vehicles (UAVs).

[0029] The technical solutions provided by the embodiments of this disclosure may include the following beneficial effects:

[0030] In the embodiments of this disclosure, the data rate reduction method for low-speed target detection at high repetition rates described above, on the one hand, reduces the original number of N PRI pulse accumulation points to a single equivalent PRI data by adding and merging the data of M adjacent PRI pulses within a CPI coherent accumulation time. Each pulse undergoes coherent accumulation, reducing the data rate to its original value. While reducing data transmission bandwidth and computational load, this method achieves minimal signal-to-noise ratio loss for low-speed target detection. Furthermore, by minimizing the signal-to-noise ratio loss for low-speed target detection, the data rate for detecting low-speed targets such as UAVs is reduced at high repetition rates, further decreasing data transmission bandwidth and computational load, thus shortening computation time and saving hardware costs. Attached Figure Description

[0031] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure. It is obvious that the drawings described below are merely some embodiments of this disclosure, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0032] Figure 1 The diagram illustrates the steps of a method for reducing data rate at high repetition rate for low-speed target detection in an exemplary embodiment of this disclosure;

[0033] Figure 2 This illustrates the odd and even slow time dimensions for Doppler sampling in an exemplary embodiment of this disclosure;

[0034] Figure 3 A schematic diagram illustrating vector addition in an exemplary embodiment of this disclosure is shown;

[0035] Figure 4 This diagram illustrates how, in an exemplary embodiment of this disclosure, the data of M adjacent PRIs are added and merged to form an equivalent PRI data. Detailed Implementation

[0036] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that this disclosure will be more comprehensive and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0037] Furthermore, the accompanying drawings are merely illustrative diagrams of embodiments of this disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities.

[0038] This example implementation provides a method for reducing the data rate for low-speed target detection at high repetition rates. (See reference...) Figure 1 As shown, this method for reducing the data rate for low-velocity target detection at high repetition rates may include:

[0039] Step S101: Obtain pulse accumulation data for N pulse repetition intervals PRI within a coherent processing interval CPI; where N is a positive integer;

[0040] Step S102: Add and merge the pulse accumulation data of N PRIs according to the data of adjacent M PRIs to form an equivalent PRI data, thereby reducing the number of pulse accumulation points of the original N PRIs to Each pulse is used for coherent accumulation, reducing the data rate to 1 / M of the original value; where M is a positive integer.

[0041] The above-mentioned method for reducing the data rate at high repetition rates for low-speed target detection involves, on the one hand, combining the N PRI pulse accumulation points within a CPI coherent accumulation time into an equivalent PRI data set by adding and merging the data from M adjacent PRI pulses, thus reducing the original N PRI pulse accumulation points to [missing information]. Each pulse undergoes coherent accumulation, reducing the data rate to its original value. While reducing data transmission bandwidth and computational load, this method achieves minimal signal-to-noise ratio loss for low-speed target detection. Furthermore, by minimizing the signal-to-noise ratio loss for low-speed target detection, the data rate for detecting low-speed targets such as UAVs is reduced at high repetition rates, further decreasing data transmission bandwidth and computational load, thus shortening computation time and saving hardware costs.

[0042] Below, we will refer to Figures 1 to 4 The steps of the above-described method for reducing the data rate for low-speed target detection at high repetition rate in this example embodiment will be described in more detail.

[0043] In one embodiment, in the target detection processing architecture, signal preprocessing parts such as digital downconversion and pulse compression are often implemented using FPGA logic resources, while signal postprocessing such as coherent accumulation is implemented using CPU, DSP, or GPU. At high pulse repetition frequencies, a large data bus bandwidth is required to transmit data. The data of adjacent M PRIs are added and merged into an equivalent PRI data for use, which reduces the bus bandwidth requirement.

[0044] The analysis focuses on the summation of PRI data when M equals 2; the principle remains the same for other M values. For N data points to be coherently accumulated, after two extractions, they are divided into odd-numbered and even-numbered columns. Let the odd-numbered column be... Even-numbered columns are The Discrete Fourier Transform (DFT) has linear properties; the FFT of an odd-numbered sequence plus an even-numbered sequence is equal to the sum of their respective FFTs.

[0045]

[0046] Moving targets produce the Doppler effect. The Doppler frequency of the target appears as a sine wave in the slow time dimension. The greater the target speed, the higher the frequency. In the extreme case, when the target speed is 0, the Doppler frequency is 0, and the sine wave appears as a straight line.

[0047] Let the PRI period be Slow time dimension sampling frequency The target Doppler frequency is Then the phase difference between adjacent odd and even sampling points is The phase difference between the FFT results obtained by performing an FFT on odd-numbered sampling points and an FFT on even-numbered sampling points is also 1. .

[0048] See Figure 2 and Figure 3 According to the principle of vector addition, when the phase difference angle between two vectors is acute, the magnitude of the resulting vector is greater than either one. As the angle increases (corresponding to an increase in target velocity), the resulting value gradually decreases. In the extreme case of a 180-degree angle (where the target velocity is half of the first blind speed), the summed vector is 0, and the target is completely canceled out. .

[0049] It is evident that, compared to the coherent accumulation of the original continuous high-repetition-rate N-point data, the smaller the target velocity, the smaller the signal-to-noise ratio loss after using the addition of M adjacent data points.

[0050] Based on the above analysis, a method for reducing the data rate for low-speed target detection at high repetition rates is proposed. This method combines the N PRI pulse accumulation points within a CPI coherent accumulation time into an equivalent PRI data set by adding and merging the data from M adjacent PRI pulses. This reduces the original N PRI pulse accumulation points to [missing data]. Each pulse undergoes coherent accumulation, reducing the data rate to its original value. While reducing data transmission bandwidth and computational load, it also results in minimal signal-to-noise ratio loss for detecting low-speed targets.

[0051] The steps to implement this application are as follows:

[0052] (1) Based on the PRI cycle within one CPI of the radar system The parameters such as the number of PRIs N, channel sampling rate, number of channels, and single-channel data bit width are used to evaluate whether the data transmission time and computation time of the signal processing system using the original N consecutive PRI data meet the requirements for slow target detection.

[0053] (1) Taking the signal processing system architecture of FPGA and GPU interconnection as an example, the calculation method is the same for other interconnection methods. The FPGA and GPU are connected through a PCIe x4 bus with an effective data transfer rate of 10.0Gbps. The channel sampling rate is 40MHz, the number of channels is 32, and the channel bit width is 64bits, then the data rate is The PCIe bus bandwidth of 10.0Gbps is insufficient to meet the transmission requirements; a different bandwidth is needed. , This indicates rounding up. Assume the GPU's computation time for processing N pulses is... For every reduction of halving, the pulse processing time is correspondingly halved, therefore, the required time is... .

[0054] (2) Based on the radar system parameters, evaluate whether the signal-to-noise ratio loss at different velocities due to the summation of M adjacent PRI data meets the radar performance signal-to-noise ratio requirements. Detection requirements: assuming the target velocity is... The system's unambiguous speed is The formula for calculating the signal-to-noise ratio loss is: Let the signal-to-noise ratio of the target echo after processing the original N pulses be... The minimum detectable signal-to-noise ratio of the system is Then the choice of M must satisfy .

[0055] (3) Select the smallest M value that satisfies both (1) and (2).

[0056] The beneficial effects of this application are: with minimal loss of signal-to-noise ratio in detecting low-speed targets, it reduces the data rate for detecting low-speed targets such as UAVs at high repetition rates, thereby reducing data transmission bandwidth, computational load, and processing time, and saving hardware costs.

[0057] In a specific embodiment, within FPGA logic resources, the specific implementation steps of the method proposed in this application are as follows:

[0058] (1) Define a loop counter cnt_pri_num, whose value is in The count cycles through the loop, resetting to zero when the CPI pulse signal is valid and incrementing by 1 when the PRI pulse signal is valid. If the design does not have a CPI pulse signal, the count is set to zero at the specified interval. Automatically returns to zero;

[0059] (2) Instantiate the internal RAM module to cache PRI data, with a depth greater than or equal to the number of valid data within a PRI;

[0060] (3) Generate RAM read / write addresses. When the cnt_pri_num count value is 0, write the PRI data to RAM. When the cnt_pri_num count value is 0, write the PRI data to RAM. The RAM value is read out and added to the current PRI data to form a valid PRI data after merging. This data is then sent to the CPU, DSP, or GPU for target detection processing via the SRIO bus or PCIe bus. When cnt_pri_num is any other value, the RAM is read out, added to the current PRI data, and then written back to the RAM cache.

[0061] (4) After processing all PRI data within a CPI using step (3), a total of One valid pulse data.

[0062] like Figure 4 The diagram shows how adding and merging M adjacent PRI data points can be equivalent to creating a single PRI data point.

[0063] It can be seen that reducing the data rate of the PRI (Primary Rank) when detecting low-speed targets at high repetition rates reduces data transmission bandwidth and computational load, thus saving hardware costs. This method combines the data from M adjacent high-repetition-rate PRIs into an equivalent low-repetition-rate PRI, reducing the data rate to the original level. Then, the signal is sent to the CPU, DSP or GPU for target detection processing via the SRIO bus or PCIe bus. This reduces the data transmission bandwidth and computational load, while minimizing the signal-to-noise ratio loss for low-speed target detection and saving hardware costs.

[0064] The above-mentioned method for reducing the data rate at high repetition rates for low-speed target detection involves, on the one hand, combining the N PRI pulse accumulation points within a CPI coherent accumulation time into an equivalent PRI data set by adding and merging the data from M adjacent PRI pulses, thus reducing the original N PRI pulse accumulation points to [missing information]. Each pulse undergoes coherent accumulation, reducing the data rate to its original value. While reducing data transmission bandwidth and computational load, this method achieves minimal signal-to-noise ratio loss for low-speed target detection. Furthermore, by minimizing the signal-to-noise ratio loss for low-speed target detection, the data rate for detecting low-speed targets such as UAVs is reduced at high repetition rates, further decreasing data transmission bandwidth and computational load, thus shortening computation time and saving hardware costs.

[0065] It should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," and "counterclockwise" in the above description indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing the embodiments of this disclosure and simplifying the description, and are not intended to indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the embodiments of this disclosure.

[0066] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of embodiments of this disclosure, "a plurality of" means two or more, unless otherwise explicitly specified.

[0067] In the embodiments of this disclosure, unless otherwise expressly specified and limited, the terms "installation," "connection," "linking," "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this disclosure according to the specific circumstances.

[0068] In embodiments of this disclosure, unless otherwise expressly specified and limited, "above" or "below" the second feature can include direct contact between the first and second features, or contact between the first and second features through another feature between them. Furthermore, "above," "over," and "on top" of the second feature includes the first feature being directly above or diagonally above the second feature, or simply indicates that the first feature is at a higher horizontal level than the second feature. "Below," "below," and "under" the second feature includes the first feature being directly below or diagonally below the second feature, or simply indicates that the first feature is at a lower horizontal level than the second feature.

[0069] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this disclosure. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. In addition, those skilled in the art can combine and integrate the different embodiments or examples described in this specification.

[0070] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the appended claims.

Claims

1. A method for reducing the data rate for low-speed target detection at high repetition rates, characterized in that, include: Obtain the pulse accumulation data of N pulse repetition intervals PRI within a coherent processing interval CPI; where N is a positive integer; The pulse accumulation data of N PRIs are added together by combining the data of the adjacent M PRIs to form an equivalent PRI dataset, thereby reducing the number of pulse accumulation points of the original N PRIs to [number missing]. Each pulse is used for coherent accumulation, reducing the data rate to 1 / M of the original value; where M is a positive integer. The conditions for determining M are as follows: assess whether the data transmission time and computation time when using the original N PRI data meet the system requirements. If not, determine the value of M based on the system's data transmission bandwidth and processing capability to reduce the data rate; assess whether the signal-to-noise ratio loss for targets with different speeds after adding and merging adjacent M PRI data meets the radar detection performance requirements; select the minimum value of M that simultaneously satisfies the above two conditions. The assessment of whether the data transmission time meets the requirements includes: calculating the data rate of the original N PRI data based on the radar system's channel sampling rate, number of channels, single-channel data bit width, and effective data transmission rate of the system bus; comparing the data rate of the original N PRI data with the system bus bandwidth to determine whether the data rate needs to be reduced; The evaluation of whether the calculation time meets the requirements includes: setting the processing time to be halved for every reduction of the number of pulses; and determining the number of pulses to be reduced by combining the calculation time of the processing unit for N pulses, thereby calculating the value of M. Evaluating whether the signal-to-noise ratio (SNR) loss meets the radar detection performance requirements includes: calculating the SNR loss based on the target velocity and the system's unambiguous velocity; and ensuring that the SNR after loss is greater than the system's minimum detectable SNR. The expression for signal-to-noise ratio loss is: in, For the target speed, For the system to be unambiguous speed.

2. The method for reducing data rate for low-speed target detection at high repetition rate according to claim 1, characterized in that, The choice of M must satisfy: in, For radar performance signal-to-noise ratio, The target echo signal-to-noise ratio is the result of processing the original N pulses.

3. The method for reducing data rate for low-speed target detection at high repetition rate according to claim 2, characterized in that, The radar system is used for detecting low-speed targets by UAVs.

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